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Refactor transform functions and update node mappers

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- Moved and redefined transform functions for KSampler, EmptyLatentImage, CLIPTextEncode, and FluxGuidance to improve organization and maintainability.
- Updated NODE_MAPPERS to include new input tracking for clip_skip in KSampler and added new transform functions for LatentUpscale and CLIPSetLastLayer.
- Enhanced the transform_sampler_custom_advanced function to handle clip_skip extraction from model inputs.
This commit is contained in:
Will Miao
2025-04-02 17:01:10 +08:00
parent aec218ba00
commit 234c942f34
2 changed files with 130 additions and 93 deletions
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128
py/workflow/ext/comfyui_core.py
View File

@@ -94,6 +94,7 @@ def transform_sampler_custom_advanced(inputs: Dict) -> Dict:
if "model" in guider and isinstance(guider["model"], dict):
result["checkpoint"] = guider["model"].get("checkpoint", "")
result["loras"] = guider["model"].get("loras", "")
result["clip_skip"] = str(int(guider["model"].get("clip_skip", "-1")) * -1)
# Extract dimensions from latent_image
if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
@@ -107,6 +108,73 @@ def transform_sampler_custom_advanced(inputs: Dict) -> Dict:
return result
def transform_ksampler(inputs: Dict) -> Dict:
"""Transform function for KSampler nodes"""
result = {
"seed": str(inputs.get("seed", "")),
"steps": str(inputs.get("steps", "")),
"cfg": str(inputs.get("cfg", "")),
"sampler": inputs.get("sampler_name", ""),
"scheduler": inputs.get("scheduler", ""),
}
# Process positive prompt
if "positive" in inputs:
result["prompt"] = inputs["positive"]
# Process negative prompt
if "negative" in inputs:
result["negative_prompt"] = inputs["negative"]
# Get dimensions from latent image
if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
width = inputs["latent_image"].get("width", 0)
height = inputs["latent_image"].get("height", 0)
if width and height:
result["size"] = f"{width}x{height}"
# Add clip_skip if present
if "clip_skip" in inputs:
result["clip_skip"] = str(inputs.get("clip_skip", ""))
# Add guidance if present
if "guidance" in inputs:
result["guidance"] = str(inputs.get("guidance", ""))
# Add model if present
if "model" in inputs:
result["checkpoint"] = inputs.get("model", {}).get("checkpoint", "")
result["loras"] = inputs.get("model", {}).get("loras", "")
result["clip_skip"] = str(inputs.get("model", {}).get("clip_skip", -1) * -1)
return result
def transform_empty_latent(inputs: Dict) -> Dict:
"""Transform function for EmptyLatentImage nodes"""
width = inputs.get("width", 0)
height = inputs.get("height", 0)
return {"width": width, "height": height, "size": f"{width}x{height}"}
def transform_clip_text(inputs: Dict) -> Any:
"""Transform function for CLIPTextEncode nodes"""
return inputs.get("text", "")
def transform_flux_guidance(inputs: Dict) -> Dict:
"""Transform function for FluxGuidance nodes"""
result = {}
if "guidance" in inputs:
result["guidance"] = inputs["guidance"]
if "conditioning" in inputs:
conditioning = inputs["conditioning"]
if isinstance(conditioning, str):
result["prompt"] = conditioning
else:
result["prompt"] = "Unknown prompt"
return result
def transform_unet_loader(inputs: Dict) -> Dict:
"""Transform function for UNETLoader node"""
unet_name = inputs.get("unet_name", "")
@@ -117,6 +185,27 @@ def transform_checkpoint_loader(inputs: Dict) -> Dict:
ckpt_name = inputs.get("ckpt_name", "")
return {"checkpoint": ckpt_name} if ckpt_name else {}
def transform_latent_upscale_by(inputs: Dict) -> Dict:
"""Transform function for LatentUpscaleBy node"""
result = {}
width = inputs["samples"].get("width", 0) * inputs["scale_by"]
height = inputs["samples"].get("height", 0) * inputs["scale_by"]
result["width"] = width
result["height"] = height
result["size"] = f"{width}x{height}"
return result
def transform_clip_set_last_layer(inputs: Dict) -> Dict:
"""Transform function for CLIPSetLastLayer node"""
result = {}
if "stop_at_clip_layer" in inputs:
result["clip_skip"] = inputs["stop_at_clip_layer"]
return result
# =============================================================================
# Node Mapper Definitions
# =============================================================================
@@ -128,6 +217,31 @@ NODE_MAPPERS_EXT = {
"inputs_to_track": ["noise", "guider", "sampler", "sigmas", "latent_image"],
"transform_func": transform_sampler_custom_advanced
},
"KSampler": {
"inputs_to_track": [
"seed", "steps", "cfg", "sampler_name", "scheduler",
"denoise", "positive", "negative", "latent_image",
"model", "clip_skip"
],
"transform_func": transform_ksampler
},
# ComfyUI core nodes
"EmptyLatentImage": {
"inputs_to_track": ["width", "height", "batch_size"],
"transform_func": transform_empty_latent
},
"EmptySD3LatentImage": {
"inputs_to_track": ["width", "height", "batch_size"],
"transform_func": transform_empty_latent
},
"CLIPTextEncode": {
"inputs_to_track": ["text", "clip"],
"transform_func": transform_clip_text
},
"FluxGuidance": {
"inputs_to_track": ["guidance", "conditioning"],
"transform_func": transform_flux_guidance
},
"RandomNoise": {
"inputs_to_track": ["noise_seed"],
"transform_func": transform_random_noise
@@ -156,8 +270,16 @@ NODE_MAPPERS_EXT = {
"inputs_to_track": ["ckpt_name"],
"transform_func": transform_checkpoint_loader
},
"CheckpointLoader": {
"inputs_to_track": ["ckpt_name"],
"transform_func": transform_checkpoint_loader
"LatentUpscale": {
"inputs_to_track": ["width", "height"],
"transform_func": transform_empty_latent
},
"LatentUpscaleBy": {
"inputs_to_track": ["samples", "scale_by"],
"transform_func": transform_latent_upscale_by
},
"CLIPSetLastLayer": {
"inputs_to_track": ["clip", "stop_at_clip_layer"],
"transform_func": transform_clip_set_last_layer
}
}

95
py/workflow/mappers.py
View File

@@ -97,55 +97,7 @@ def process_node(node_id: str, node_data: Dict, workflow: Dict, parser: 'Workflo
# Transform Functions
# =============================================================================
def transform_ksampler(inputs: Dict) -> Dict:
"""Transform function for KSampler nodes"""
result = {
"seed": str(inputs.get("seed", "")),
"steps": str(inputs.get("steps", "")),
"cfg": str(inputs.get("cfg", "")),
"sampler": inputs.get("sampler_name", ""),
"scheduler": inputs.get("scheduler", ""),
}
# Process positive prompt
if "positive" in inputs:
result["prompt"] = inputs["positive"]
# Process negative prompt
if "negative" in inputs:
result["negative_prompt"] = inputs["negative"]
# Get dimensions from latent image
if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
width = inputs["latent_image"].get("width", 0)
height = inputs["latent_image"].get("height", 0)
if width and height:
result["size"] = f"{width}x{height}"
# Add clip_skip if present
if "clip_skip" in inputs:
result["clip_skip"] = str(inputs.get("clip_skip", ""))
# Add guidance if present
if "guidance" in inputs:
result["guidance"] = str(inputs.get("guidance", ""))
# Add model if present
if "model" in inputs:
result["checkpoint"] = inputs.get("model", {}).get("checkpoint", "")
result["loras"] = inputs.get("model", {}).get("loras", "")
return result
def transform_empty_latent(inputs: Dict) -> Dict:
"""Transform function for EmptyLatentImage nodes"""
width = inputs.get("width", 0)
height = inputs.get("height", 0)
return {"width": width, "height": height, "size": f"{width}x{height}"}
def transform_clip_text(inputs: Dict) -> Any:
"""Transform function for CLIPTextEncode nodes"""
return inputs.get("text", "")
def transform_lora_loader(inputs: Dict) -> Dict:
"""Transform function for LoraLoader nodes"""
@@ -181,6 +133,9 @@ def transform_lora_loader(inputs: Dict) -> Dict:
"checkpoint": inputs.get("model", {}).get("checkpoint", ""),
"loras": " ".join(lora_texts)
}
if "clip" in inputs:
result["clip_skip"] = inputs["clip"].get("clip_skip", "-1")
return result
@@ -241,56 +196,16 @@ def transform_trigger_word_toggle(inputs: Dict) -> str:
return ", ".join(active_words)
def transform_flux_guidance(inputs: Dict) -> Dict:
"""Transform function for FluxGuidance nodes"""
result = {}
if "guidance" in inputs:
result["guidance"] = inputs["guidance"]
if "conditioning" in inputs:
conditioning = inputs["conditioning"]
if isinstance(conditioning, str):
result["prompt"] = conditioning
else:
result["prompt"] = "Unknown prompt"
return result
# =============================================================================
# Node Mapper Definitions
# =============================================================================
# Central definition of all supported node types and their configurations
NODE_MAPPERS = {
# ComfyUI core nodes
"KSampler": {
"inputs_to_track": [
"seed", "steps", "cfg", "sampler_name", "scheduler",
"denoise", "positive", "negative", "latent_image",
"model", "clip_skip"
],
"transform_func": transform_ksampler
},
"EmptyLatentImage": {
"inputs_to_track": ["width", "height", "batch_size"],
"transform_func": transform_empty_latent
},
"EmptySD3LatentImage": {
"inputs_to_track": ["width", "height", "batch_size"],
"transform_func": transform_empty_latent
},
"CLIPTextEncode": {
"inputs_to_track": ["text", "clip"],
"transform_func": transform_clip_text
},
"FluxGuidance": {
"inputs_to_track": ["guidance", "conditioning"],
"transform_func": transform_flux_guidance
},
# LoraManager nodes
"Lora Loader (LoraManager)": {
"inputs_to_track": ["model", "loras", "lora_stack"],
"inputs_to_track": ["model", "clip", "loras", "lora_stack"],
"transform_func": transform_lora_loader
},
"Lora Stacker (LoraManager)": {